Fog-Robot Network Semantic Resource Discovery
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Solution Overview
Problem
Conventional fog-robot networks rely on cloud-based architectures, leading to real-time knowledge sharing latency and dependency on constant cloud connectivity, which is not feasible in outdoor emergency situations where immediate task execution is critical without human intervention.
Innovation Solution
Implementing a processor-based method for dynamic semantic resource discovery in fog-robot networks using an ontology-based semantic knowledge repository, leveraging edge computing and peer-to-peer networks to capture and update resource data and tasks in real-time, enabling task allocation across heterogeneous resources without cloud dependency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based architecture is used for fog-robot networks, then centralized knowledge management is achieved, but real-time knowledge sharing latency increases and dependency on constant cloud connectivity is created
Solution Approach 1:
The patent segments the centralized cloud-based knowledge repository into distributed semantic knowledge repositories located at each fog node and robot. This segmentation eliminates the single point of failure (cloud dependency) and enables local real-time knowledge sharing without latency, as each node maintains its own knowledge base while sharing through peer-to-peer communication.
Solution Approach 2:
The patent introduces an ontology-based semantic messaging system as an intermediary layer that enables direct peer-to-peer communication between fog nodes and robots. This intermediary uses semantic web technologies (RDF, OWL) to structure knowledge exchange, allowing nodes to discover and share resources autonomously without cloud mediation, thus reducing latency and eliminating connectivity dependency.
2Measurement precision
If dynamic semantic resource discovery is implemented, then real-time task allocation accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements a universal ontology-based semantic framework that serves multiple functions simultaneously: resource description, task specification, knowledge sharing, and task allocation. This multi-functional approach uses standardized semantic web ontologies (ROS2 ontology, Task Ontology, Resource Ontology) that can represent diverse robot resources and tasks uniformly, improving discovery accuracy without proportionally increasing complexity through reuse of the same semantic infrastructure.
Solution Approach 2:
The patent changes the parameter representation from traditional rigid resource descriptors to dynamic semantic parameters using ontology-based descriptions. Resources are described with semantic attributes (capabilities, states, locations) that can be dynamically queried and matched with task requirements, enabling precise real-time resource discovery while leveraging existing semantic web toolkits to manage the complexity.
3Productivity
If peer-to-peer semantic knowledge sharing is enabled, then real-time collaboration among robots improves, but communication overhead increases
Solution Approach 1:
The patent implements partial knowledge sharing where robots and fog nodes exchange only the specific semantic knowledge relevant to current tasks rather than complete knowledge bases. The semantic messaging system allows selective publication and subscription to task-related resources, sub-tasks, and capabilities, reducing communication overhead while maintaining real-time collaboration for active missions.
4Loss of time
If ontology-based semantic knowledge repository is used, then resource data structuring and retrieval efficiency improve, but initial system setup complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining standardized ontologies (ROS2 ontology, Task Ontology, Resource Ontology) that can be directly instantiated and used without creating custom ontologies from scratch. These pre-built semantic frameworks provide ready-to-use class structures, properties, and relationships for robot resources and tasks, reducing initial setup complexity while enabling efficient structured data retrieval through semantic querying capabilities.
Data Source
AI summary
Systems and methods of the present disclosure enable exchange of semantic knowledge of resource data and task data between heterogeneous resources in a constrained environment wherein cloud infrastructure and cloud based knowledge repository is not available. Ontology based semantic knowledge exchange firstly enables discovery of available resources in real time. New tasks may evolve at runtime and so also resource data associated with the resources may vary over time. Systems and methods of the present disclosure effectively address these dynamic logistics in a constrained environment involving heterogeneous resources. Furthermore, based on the required resource data for each task and the available resources discovered in real time, task allocation can be effectively handled.